Chapter 9 Advanced R and the MAUP problem

The Modifiable Areal Unit Problem des

Use air b n b at lower london scales - ward etc

Here we will be using London brough and ward data from practical 1. As we’re getting better with R, we will try to automate almost everthing — meaning that if you gave this code to someone else they could just run it without any data files and generate the same result.

1. Download and unzip the London statistical gis boundaries.

# make a temp file to store the .zip in
temp <- tempfile()
download.file("https://data.london.gov.uk/download/statistical-gis-boundary-files-london/9ba8c833-6370-4b11-abdc-314aa020d5e0/statistical-gis-boundaries-london.zip",temp)
# unzip data into a folder (exdir) of your choice
data <- unzip(temp, exdir="prac10_data")
# unlink the temp file
unlink(temp)

2. Take the downloaded data and filter it based on the filename that countains: Borough OR Ward_ AND .shp using grepl()

t<-grepl("Borough|Ward_", data) & grepl(".shp$", data)
t

##  [1] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
## [12] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [23] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
## [34] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [45] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [56] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [67] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [78] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [89] FALSE FALSE FALSE FALSE FALSE FALSE FALSE

t just gives an index of where the values are equal to what we specified.

3. We then need to use it to subset from our original data…

boroughsandwards<-data[t]

4. Now read in both of the files using lapply() that applies a function (here st_read()) to a list

boroughsandwardssf<-lapply(boroughsandwards, st_read)

## Reading layer `London_Borough_Excluding_MHW' from data source `C:\Users\ucfnmac\OneDrive - University College London\Teaching\CASA0005repo\prac10_data\statistical-gis-boundaries-london\ESRI\London_Borough_Excluding_MHW.shp' using driver `ESRI Shapefile'
## Simple feature collection with 33 features and 7 fields
## geometry type:  MULTIPOLYGON
## dimension:      XY
## bbox:           xmin: 503568.2 ymin: 155850.8 xmax: 561957.5 ymax: 200933.9
## epsg (SRID):    NA
## proj4string:    +proj=tmerc +lat_0=49 +lon_0=-2 +k=0.999601272 +x_0=400000 +y_0=-100000 +datum=OSGB36 +units=m +no_defs
## Reading layer `London_Ward_CityMerged' from data source `C:\Users\ucfnmac\OneDrive - University College London\Teaching\CASA0005repo\prac10_data\statistical-gis-boundaries-london\ESRI\London_Ward_CityMerged.shp' using driver `ESRI Shapefile'
## Simple feature collection with 625 features and 7 fields
## geometry type:  POLYGON
## dimension:      XY
## bbox:           xmin: 503568.2 ymin: 155850.8 xmax: 561957.5 ymax: 200933.9
## epsg (SRID):    NA
## proj4string:    +proj=tmerc +lat_0=49 +lon_0=-2 +k=0.999601272 +x_0=400000 +y_0=-100000 +datum=OSGB36 +units=m +no_defs

To map or access each individual shapefile it’s just…

qtm(boroughsandwardssf[[2]]) 

# or change to 1 

5. Get the data for Airbnb

Airbnb <- read_csv("http://data.insideairbnb.com/united-kingdom/england/london/2019-07-10/visualisations/listings.csv")

6. And for OSM

library(tidyverse)

## -- Attaching packages --- tidyverse 1.2.1 --

## v tibble  2.1.3     v dplyr   0.8.3
## v tidyr   0.8.3     v stringr 1.4.0
## v purrr   0.3.2     v forcats 0.4.0

## -- Conflicts ------ tidyverse_conflicts() --
## x dplyr::filter() masks plotly::filter(), stats::filter()
## x dplyr::lag()    masks stats::lag()

library(osmdata)

## Data (c) OpenStreetMap contributors, ODbL 1.0. http://www.openstreetmap.org/copyright

library(sf)

## Linking to GEOS 3.6.1, GDAL 2.2.3, PROJ 4.9.3

library(ggmap)

## Google's Terms of Service: https://cloud.google.com/maps-platform/terms/.

## Please cite ggmap if you use it! See citation("ggmap") for details.

## 
## Attaching package: 'ggmap'

## The following object is masked from 'package:plotly':
## 
##     wind

q <- getbb("London")%>%
      opq()%>%
       add_osm_feature(key="pois", value="hotel")
Londonhotels <- osmdata_sf(q)

library(memisc)
temp2 <- tempfile()
download.file("http://download.geofabrik.de/europe/great-britain/england/greater-london-latest-free.shp.zip
",mode='wb', temp2)
# unzip into data
data <- unzip(temp2, exdir="prac10_data")


# unlink the temp file
unlink(temp2)

src <- osmsource_api(url = "https://api.openstreetmap.org/api/0.6/")
bb <- center_bbox(-0.112510,51.507627, 1000, 1000)
ctown <- get_osm(bb, source = src)
plot(ctown)
points(-1.53492, 53.81934, col = "red", lwd = 5)

summary(ctown$ways)

library(timevis)

data <- data.frame(
  id      = 1:4,
  content = c("Item one", "Item two",
              "Ranged item", "Item four"),
  start   = c("2016-01-10", "2016-01-11",
              "2016-01-20", "2016-02-14 15:00:00"),
  end     = c(NA, NA, "2016-02-04", NA)
)

timevis(data)